The present invention generally relates to process control methodologies, and particularly to a process control method and system which seeks to optimize real-time control by using a model and a variable prediction mechanism to anticipate and respond to future process events.
In order to achieve maximum economic efficiency and optimum product quality, the demands for more comprehensive process control automation have continued to increase in both quantity and sophistication. In this regard, substantial advances have been made in terms of the ability to rapidly acquire input data from a multitude of sensors and generate highly reliable output commands for controlling a physical process. However, most process control methodologies have traditionally relied on the use of feedback signals to steer one or more proportional-integral-derivative ("PID") algorithms, as a way to achieve a desired set-point. While this approach to process control has been unquestionably effective, it is primarily reactive in nature. Accordingly, the use of feedback-based control may lead to a sluggish overall system response and/or cause an adjustment to be made that would be larger than would otherwise be desirable.
A chemical batch reactor provides one example of a process control challenge where feedback-based control may not consistently achieve an optimum result. In this regard, batch reactor control will typically involve moving set-points, process delays, large inertia, non-linearities, unmeasured disturbances and multiple control efforts. Accordingly, information from the sensors as to the current state of the batch reactor process is important in terms of achieving the existing set-point, but this information may not be sufficient, by itself, to achieve a subsequent set-point without encountering an unwanted delay in the change of a process parameter, such as temperature or pressure. While a dynamic lag in a process parameter may be overcome by bringing about a significant change in a manipulated parameter (e.g., a heating element), it is generally considered less desirable to force large changes on a process control system.
Additionally, one of the key goals of any batch reactor control process is the ability to minimize product variability from one batch to another. For example, when molecular distribution is used as a grading factor in the production of a polymer, the consistent achievement of a particular molecular distribution profile may well have substantial economic implications on the investment in the production equipment. Accordingly, a continuing need exists to develop process control methodologies which are capable of minimizing product variability in batch reactor processes, as well as other varying process control applications.
Accordingly, it is a principal objective of the present invention to provide a method and system of process control which seeks to optimize the control methodology by anticipating future process events or states and selecting the most beneficial set of control changes.
It is a more specific objective of the present invention to provide a method and system of process control which utilizes a model of the process and an variable prediction mechanism to anticipate and respond to events that will occur during a reaction.
It is another objective of the present invention to provide a model predictive controller which not only seeks to minimize product variability, but also seeks to achieve a more economical and efficient production process.
It is a further objective of the present invention to provide a model predictive controller which runs in real-time, and has the capability to predict and control existing process conditions which are not otherwise measured by the process control equipment.
It is yet another objective of the present invention to provide a model predictive controller which is capable of predicting future deviations from the model on the basis of a trajectory of past and current deviations or errors, as well as modifying set-point profiles on the basis of past disturbances.
It is an additional objective of the present invention to provide a model predictive controller which facilitates a systematic transfer of process knowledge gained through plant experience or research.
It is still another objective of the present invention to provide a model predictive controller which will shift the emphasis of control from measured process quantities to the monitoring and control of the unmeasured process quantities that will have a direct effect on product properties.